Talia Sharmeen

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Verbatim text

COMPUTER VISION BASED TUNNEL MONITORING SYSTEM
This project aims to develop an intelligent, edge-AI based tunnel monitoring system designed to enhance safety and traffic management in road tunnels. Tunnels are high-risk environments where tire outbreaks, vehicle collisions, and congestion can escalate quickly due to confined space and poor communication mediums. The proposed system addresses the lack of real-time automated monitoring by using computer vision and long-range communication.
The proposed system uses YOLO models deployed on the NVIDIA Jetson to identify traffic congestion, fire, and vehicle collisions. Sensor nodes built using ESP32 will collect environmental data and communicate with the gateway using LoRa, enabling long range transmission. Detected events and data will be stored in the cloud, where a web-based application provides real-time data and alerts for authorities.
Key Words: NVIDIA Jetson, LoRa, Computer-Vision, ESP32, IoT
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1.Real-timv Fire Morvt.orrf
2.Edge Alf« Traffie An3fySS.
3.Lcng-f?!II"@& (LeiR,}E-v,ergen:y Awt&
4.Automated.Authorit) Notifications
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~'.E'l"Vision.
Technology Used:
NVIDIA Jetson, LoRa, ESP32, Ultralytics, OpenCV, Python
Supervisor Name:
Dr. Muhammad Tariq
Group Members:
Muhammad Ali Majid (22i-9874)
Ahmed Waqar (22i-2217)
Talia Sharmeen (22i-1819)
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